Overview

Dataset statistics

Number of variables15
Number of observations1134
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory133.0 KiB
Average record size in memory120.1 B

Variable types

Categorical4
Numeric11

Alerts

2010 is highly overall correlated with 2011 and 9 other fieldsHigh correlation
2011 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2012 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2013 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2014 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2015 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2016 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2017 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2018 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2019 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2020 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
Masseinheit is uniformly distributedUniform
Leistung is uniformly distributedUniform
Geschlecht is uniformly distributedUniform
Altersklasse is uniformly distributedUniform

Reproduction

Analysis started2022-12-28 08:18:36.339637
Analysis finished2022-12-28 08:18:43.241202
Duration6.9 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Masseinheit
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Absolutwert
567 
Wert pro Kopf und Monat
567 

Length

Max length23
Median length17
Mean length17
Min length11

Characters and Unicode

Total characters19278
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAbsolutwert
2nd rowAbsolutwert
3rd rowAbsolutwert
4th rowAbsolutwert
5th rowAbsolutwert

Common Values

ValueCountFrequency (%)
Absolutwert 567
50.0%
Wert pro Kopf und Monat 567
50.0%

Length

2022-12-28T09:18:43.271356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T09:18:43.308884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
absolutwert 567
16.7%
wert 567
16.7%
pro 567
16.7%
kopf 567
16.7%
und 567
16.7%
monat 567
16.7%

Most occurring characters

ValueCountFrequency (%)
o 2268
11.8%
t 2268
11.8%
2268
11.8%
r 1701
 
8.8%
p 1134
 
5.9%
u 1134
 
5.9%
n 1134
 
5.9%
e 1134
 
5.9%
M 567
 
2.9%
d 567
 
2.9%
Other values (9) 5103
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14742
76.5%
Space Separator 2268
 
11.8%
Uppercase Letter 2268
 
11.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2268
15.4%
t 2268
15.4%
r 1701
11.5%
p 1134
7.7%
u 1134
7.7%
n 1134
7.7%
e 1134
7.7%
d 567
 
3.8%
f 567
 
3.8%
b 567
 
3.8%
Other values (4) 2268
15.4%
Uppercase Letter
ValueCountFrequency (%)
M 567
25.0%
K 567
25.0%
A 567
25.0%
W 567
25.0%
Space Separator
ValueCountFrequency (%)
2268
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17010
88.2%
Common 2268
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2268
13.3%
t 2268
13.3%
r 1701
 
10.0%
p 1134
 
6.7%
u 1134
 
6.7%
n 1134
 
6.7%
e 1134
 
6.7%
M 567
 
3.3%
d 567
 
3.3%
f 567
 
3.3%
Other values (8) 4536
26.7%
Common
ValueCountFrequency (%)
2268
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2268
11.8%
t 2268
11.8%
2268
11.8%
r 1701
 
8.8%
p 1134
 
5.9%
u 1134
 
5.9%
n 1134
 
5.9%
e 1134
 
5.9%
M 567
 
2.9%
d 567
 
2.9%
Other values (9) 5103
26.5%

Leistung
Categorical

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Leistung - Total
126 
Stationäre Kurativbehandlung
126 
Ambulante Kurativbehandlung
126 
Rehabilitation
126 
Langzeitpflege
126 
Other values (4)
504 

Length

Max length31
Median length27
Mean length18.444444
Min length10

Characters and Unicode

Total characters20916
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeistung - Total
2nd rowLeistung - Total
3rd rowLeistung - Total
4th rowLeistung - Total
5th rowLeistung - Total

Common Values

ValueCountFrequency (%)
Leistung - Total 126
11.1%
Stationäre Kurativbehandlung 126
11.1%
Ambulante Kurativbehandlung 126
11.1%
Rehabilitation 126
11.1%
Langzeitpflege 126
11.1%
Unterstützende Dienstleistungen 126
11.1%
Gesundheitsgüter 126
11.1%
Prävention 126
11.1%
Verwaltung 126
11.1%

Length

2022-12-28T09:18:43.339352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T09:18:43.380904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
kurativbehandlung 252
14.3%
leistung 126
 
7.1%
126
 
7.1%
total 126
 
7.1%
stationäre 126
 
7.1%
ambulante 126
 
7.1%
rehabilitation 126
 
7.1%
langzeitpflege 126
 
7.1%
unterstützende 126
 
7.1%
dienstleistungen 126
 
7.1%
Other values (3) 378
21.4%

Most occurring characters

ValueCountFrequency (%)
e 2520
12.0%
t 2394
 
11.4%
n 2268
 
10.8%
i 1512
 
7.2%
a 1386
 
6.6%
u 1134
 
5.4%
g 1008
 
4.8%
l 1008
 
4.8%
r 882
 
4.2%
s 756
 
3.6%
Other values (25) 6048
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18522
88.6%
Uppercase Letter 1638
 
7.8%
Space Separator 630
 
3.0%
Dash Punctuation 126
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2520
13.6%
t 2394
12.9%
n 2268
12.2%
i 1512
8.2%
a 1386
 
7.5%
u 1134
 
6.1%
g 1008
 
5.4%
l 1008
 
5.4%
r 882
 
4.8%
s 756
 
4.1%
Other values (12) 3654
19.7%
Uppercase Letter
ValueCountFrequency (%)
L 252
15.4%
K 252
15.4%
S 126
7.7%
A 126
7.7%
R 126
7.7%
T 126
7.7%
U 126
7.7%
D 126
7.7%
G 126
7.7%
P 126
7.7%
Space Separator
ValueCountFrequency (%)
630
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20160
96.4%
Common 756
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2520
12.5%
t 2394
11.9%
n 2268
11.2%
i 1512
 
7.5%
a 1386
 
6.9%
u 1134
 
5.6%
g 1008
 
5.0%
l 1008
 
5.0%
r 882
 
4.4%
s 756
 
3.8%
Other values (23) 5292
26.2%
Common
ValueCountFrequency (%)
630
83.3%
- 126
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20412
97.6%
None 504
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2520
12.3%
t 2394
11.7%
n 2268
11.1%
i 1512
 
7.4%
a 1386
 
6.8%
u 1134
 
5.6%
g 1008
 
4.9%
l 1008
 
4.9%
r 882
 
4.3%
s 756
 
3.7%
Other values (23) 5544
27.2%
None
ValueCountFrequency (%)
ü 252
50.0%
ä 252
50.0%

Geschlecht
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Geschlecht - Total
378 
Mann
378 
Frau
378 

Length

Max length18
Median length4
Mean length8.6666667
Min length4

Characters and Unicode

Total characters9828
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGeschlecht - Total
2nd rowGeschlecht - Total
3rd rowGeschlecht - Total
4th rowGeschlecht - Total
5th rowGeschlecht - Total

Common Values

ValueCountFrequency (%)
Geschlecht - Total 378
33.3%
Mann 378
33.3%
Frau 378
33.3%

Length

2022-12-28T09:18:43.428802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T09:18:43.464849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
geschlecht 378
20.0%
378
20.0%
total 378
20.0%
mann 378
20.0%
frau 378
20.0%

Most occurring characters

ValueCountFrequency (%)
a 1134
11.5%
c 756
 
7.7%
h 756
 
7.7%
l 756
 
7.7%
t 756
 
7.7%
756
 
7.7%
e 756
 
7.7%
n 756
 
7.7%
G 378
 
3.8%
M 378
 
3.8%
Other values (7) 2646
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7182
73.1%
Uppercase Letter 1512
 
15.4%
Space Separator 756
 
7.7%
Dash Punctuation 378
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1134
15.8%
c 756
10.5%
h 756
10.5%
l 756
10.5%
t 756
10.5%
e 756
10.5%
n 756
10.5%
r 378
 
5.3%
o 378
 
5.3%
s 378
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
G 378
25.0%
M 378
25.0%
F 378
25.0%
T 378
25.0%
Space Separator
ValueCountFrequency (%)
756
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8694
88.5%
Common 1134
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1134
13.0%
c 756
 
8.7%
h 756
 
8.7%
l 756
 
8.7%
t 756
 
8.7%
e 756
 
8.7%
n 756
 
8.7%
G 378
 
4.3%
M 378
 
4.3%
r 378
 
4.3%
Other values (5) 1890
21.7%
Common
ValueCountFrequency (%)
756
66.7%
- 378
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1134
11.5%
c 756
 
7.7%
h 756
 
7.7%
l 756
 
7.7%
t 756
 
7.7%
756
 
7.7%
e 756
 
7.7%
n 756
 
7.7%
G 378
 
3.8%
M 378
 
3.8%
Other values (7) 2646
26.9%

Altersklasse
Categorical

Distinct21
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Altersklasse - Total
 
54
51-55 Jahre
 
54
91-95 Jahre
 
54
86-90 Jahre
 
54
81-85 Jahre
 
54
Other values (16)
864 

Length

Max length20
Median length11
Mean length11.571429
Min length9

Characters and Unicode

Total characters13122
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAltersklasse - Total
2nd row0-5 Jahre
3rd row6-10 Jahre
4th row11-15 Jahre
5th row16-20 Jahre

Common Values

ValueCountFrequency (%)
Altersklasse - Total 54
 
4.8%
51-55 Jahre 54
 
4.8%
91-95 Jahre 54
 
4.8%
86-90 Jahre 54
 
4.8%
81-85 Jahre 54
 
4.8%
76-80 Jahre 54
 
4.8%
71-75 Jahre 54
 
4.8%
66-70 Jahre 54
 
4.8%
61-65 Jahre 54
 
4.8%
56-60 Jahre 54
 
4.8%
Other values (11) 594
52.4%

Length

2022-12-28T09:18:43.496383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jahre 1080
44.4%
altersklasse 54
 
2.2%
0-5 54
 
2.2%
und 54
 
2.2%
96 54
 
2.2%
6-10 54
 
2.2%
11-15 54
 
2.2%
16-20 54
 
2.2%
21-25 54
 
2.2%
26-30 54
 
2.2%
Other values (16) 864
35.6%

Most occurring characters

ValueCountFrequency (%)
1296
9.9%
e 1242
9.5%
r 1188
9.1%
a 1188
9.1%
h 1134
8.6%
J 1080
 
8.2%
- 1080
 
8.2%
6 756
 
5.8%
5 756
 
5.8%
1 702
 
5.3%
Other values (18) 2700
20.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5508
42.0%
Decimal Number 4050
30.9%
Space Separator 1296
 
9.9%
Uppercase Letter 1188
 
9.1%
Dash Punctuation 1080
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1242
22.5%
r 1188
21.6%
a 1188
21.6%
h 1134
20.6%
l 162
 
2.9%
s 162
 
2.9%
t 108
 
2.0%
n 54
 
1.0%
d 54
 
1.0%
u 54
 
1.0%
Other values (3) 162
 
2.9%
Decimal Number
ValueCountFrequency (%)
6 756
18.7%
5 756
18.7%
1 702
17.3%
0 540
13.3%
4 216
 
5.3%
7 216
 
5.3%
8 216
 
5.3%
9 216
 
5.3%
3 216
 
5.3%
2 216
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
J 1080
90.9%
A 54
 
4.5%
T 54
 
4.5%
Space Separator
ValueCountFrequency (%)
1296
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6696
51.0%
Common 6426
49.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1242
18.5%
r 1188
17.7%
a 1188
17.7%
h 1134
16.9%
J 1080
16.1%
l 162
 
2.4%
s 162
 
2.4%
t 108
 
1.6%
n 54
 
0.8%
d 54
 
0.8%
Other values (6) 324
 
4.8%
Common
ValueCountFrequency (%)
1296
20.2%
- 1080
16.8%
6 756
11.8%
5 756
11.8%
1 702
10.9%
0 540
8.4%
4 216
 
3.4%
7 216
 
3.4%
8 216
 
3.4%
9 216
 
3.4%
Other values (2) 432
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1296
9.9%
e 1242
9.5%
r 1188
9.1%
a 1188
9.1%
h 1134
8.6%
J 1080
 
8.2%
- 1080
 
8.2%
6 756
 
5.8%
5 756
 
5.8%
1 702
 
5.3%
Other values (18) 2700
20.6%

2010
Real number (ℝ)

Distinct1099
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean590.92492
Minimum0.4
Maximum62564.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-12-28T09:18:43.536157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile13.999
Q139.85
median109.045
Q3352.195
95-th percentile2317.954
Maximum62564.98
Range62564.58
Interquartile range (IQR)312.345

Descriptive statistics

Standard deviation2579.3335
Coefficient of variation (CV)4.364909
Kurtosis336.26978
Mean590.92492
Median Absolute Deviation (MAD)89.885
Skewness16.116223
Sum670108.86
Variance6652961.4
MonotonicityNot monotonic
2022-12-28T09:18:43.578947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.8 4
 
0.4%
18.12 4
 
0.4%
17.98 3
 
0.3%
18.05 3
 
0.3%
17.92 3
 
0.3%
17.97 3
 
0.3%
52.92 2
 
0.2%
24.5 2
 
0.2%
78.04 2
 
0.2%
18.27 2
 
0.2%
Other values (1089) 1106
97.5%
ValueCountFrequency (%)
0.4 1
0.1%
0.79 1
0.1%
1.05 1
0.1%
1.49 1
0.1%
1.89 1
0.1%
1.95 1
0.1%
3.14 1
0.1%
3.15 1
0.1%
3.89 1
0.1%
3.93 1
0.1%
ValueCountFrequency (%)
62564.98 1
0.1%
36086.94 1
0.1%
26478.04 1
0.1%
15808.31 1
0.1%
13373.44 1
0.1%
12589.32 1
0.1%
10083.01 1
0.1%
8955.02 1
0.1%
8250.52 1
0.1%
7912.46 1
0.1%

2011
Real number (ℝ)

Distinct1090
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean609.47864
Minimum0.4
Maximum64242.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-12-28T09:18:43.624227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile14.7855
Q141.62
median113.21
Q3358.645
95-th percentile2394.112
Maximum64242.69
Range64242.29
Interquartile range (IQR)317.025

Descriptive statistics

Standard deviation2655.4806
Coefficient of variation (CV)4.3569708
Kurtosis332.6733
Mean609.47864
Median Absolute Deviation (MAD)92.605
Skewness16.002823
Sum691148.78
Variance7051577.4
MonotonicityNot monotonic
2022-12-28T09:18:43.668624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.03 4
 
0.4%
17.62 3
 
0.3%
17.79 3
 
0.3%
17.93 3
 
0.3%
17.89 3
 
0.3%
18.06 3
 
0.3%
17.81 3
 
0.3%
43.93 2
 
0.2%
14.89 2
 
0.2%
57.83 2
 
0.2%
Other values (1080) 1106
97.5%
ValueCountFrequency (%)
0.4 1
0.1%
1.24 2
0.2%
1.46 1
0.1%
1.86 1
0.1%
2.12 1
0.1%
3.15 1
0.1%
3.28 1
0.1%
3.91 1
0.1%
3.92 1
0.1%
4.16 1
0.1%
ValueCountFrequency (%)
64242.69 1
0.1%
37043.97 1
0.1%
27198.72 1
0.1%
16108.75 1
0.1%
13582.55 1
0.1%
13256.77 1
0.1%
10097.63 1
0.1%
9105.68 1
0.1%
8805.66 1
0.1%
8700.65 1
0.1%

2012
Real number (ℝ)

Distinct1095
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean628.64144
Minimum0.38
Maximum66512.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-12-28T09:18:43.717665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.38
5-th percentile14.3375
Q141.8125
median114.375
Q3374.815
95-th percentile2483.827
Maximum66512.44
Range66512.06
Interquartile range (IQR)333.0025

Descriptive statistics

Standard deviation2748.7341
Coefficient of variation (CV)4.3724991
Kurtosis332.70933
Mean628.64144
Median Absolute Deviation (MAD)93.63
Skewness16.007063
Sum712879.39
Variance7555539.3
MonotonicityNot monotonic
2022-12-28T09:18:43.762516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.56 4
 
0.4%
17.67 3
 
0.3%
17.52 3
 
0.3%
17.8 3
 
0.3%
17.71 3
 
0.3%
17.45 3
 
0.3%
17.65 3
 
0.3%
17.64 3
 
0.3%
34.6 2
 
0.2%
172.34 2
 
0.2%
Other values (1085) 1105
97.4%
ValueCountFrequency (%)
0.38 1
0.1%
0.87 1
0.1%
1.27 1
0.1%
1.42 1
0.1%
1.8 1
0.1%
2.28 1
0.1%
3.06 1
0.1%
3.32 1
0.1%
3.94 1
0.1%
3.98 1
0.1%
ValueCountFrequency (%)
66512.44 1
0.1%
38167.59 1
0.1%
28344.85 1
0.1%
16924.49 1
0.1%
14176.31 1
0.1%
13831.65 1
0.1%
10181.46 1
0.1%
9503.46 1
0.1%
9054.41 1
0.1%
8963.82 1
0.1%

2013
Real number (ℝ)

Distinct1094
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean651.40499
Minimum0.39
Maximum69118.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-12-28T09:18:43.810377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.39
5-th percentile14.715
Q144.1
median117.865
Q3392.8075
95-th percentile2532.4805
Maximum69118.03
Range69117.64
Interquartile range (IQR)348.7075

Descriptive statistics

Standard deviation2854.2571
Coefficient of variation (CV)4.3816936
Kurtosis333.42098
Mean651.40499
Median Absolute Deviation (MAD)96.845
Skewness16.027871
Sum738693.26
Variance8146783.4
MonotonicityNot monotonic
2022-12-28T09:18:43.854514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.46 4
 
0.4%
18.5 4
 
0.4%
18.2 3
 
0.3%
18.31 3
 
0.3%
18.39 3
 
0.3%
18.44 3
 
0.3%
18.28 3
 
0.3%
18.29 3
 
0.3%
18.07 3
 
0.3%
18.18 3
 
0.3%
Other values (1084) 1102
97.2%
ValueCountFrequency (%)
0.39 1
0.1%
1.17 1
0.1%
1.49 1
0.1%
1.54 1
0.1%
1.88 1
0.1%
2.49 1
0.1%
3.18 1
0.1%
3.93 1
0.1%
3.99 1
0.1%
4.12 1
0.1%
ValueCountFrequency (%)
69118.03 1
0.1%
39477.83 1
0.1%
29640.21 1
0.1%
17687.57 1
0.1%
14791.18 1
0.1%
14255.11 1
0.1%
10418.93 1
0.1%
9917.54 1
0.1%
9295.71 1
0.1%
9136.33 1
0.1%

2014
Real number (ℝ)

Distinct1095
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean669.76481
Minimum0.41
Maximum71429.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-12-28T09:18:43.900619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile14.7125
Q145.8875
median120.58
Q3405.0875
95-th percentile2578.748
Maximum71429.22
Range71428.81
Interquartile range (IQR)359.2

Descriptive statistics

Standard deviation2946.2652
Coefficient of variation (CV)4.3989549
Kurtosis334.89803
Mean669.76481
Median Absolute Deviation (MAD)99.01
Skewness16.076803
Sum759513.29
Variance8680478.5
MonotonicityNot monotonic
2022-12-28T09:18:43.943792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.79 4
 
0.4%
19.01 3
 
0.3%
19 3
 
0.3%
18.97 3
 
0.3%
19.04 3
 
0.3%
18.56 2
 
0.2%
308.98 2
 
0.2%
18.96 2
 
0.2%
120.58 2
 
0.2%
19.08 2
 
0.2%
Other values (1085) 1108
97.7%
ValueCountFrequency (%)
0.41 1
0.1%
1.24 1
0.1%
1.55 1
0.1%
1.72 1
0.1%
1.96 1
0.1%
2.72 1
0.1%
3.83 1
0.1%
3.93 1
0.1%
4.05 1
0.1%
4.17 1
0.1%
ValueCountFrequency (%)
71429.22 1
0.1%
40685.88 1
0.1%
30743.35 1
0.1%
18680.79 1
0.1%
14947.37 1
0.1%
14627.86 1
0.1%
10604.07 1
0.1%
10447.26 1
0.1%
9502 1
0.1%
9378.68 1
0.1%

2015
Real number (ℝ)

Distinct1097
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean693.87709
Minimum0.42
Maximum74384.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-12-28T09:18:43.989786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile15.145
Q147.215
median124.73
Q3416.315
95-th percentile2684.3615
Maximum74384.64
Range74384.22
Interquartile range (IQR)369.1

Descriptive statistics

Standard deviation3061.8626
Coefficient of variation (CV)4.4126872
Kurtosis337.3768
Mean693.87709
Median Absolute Deviation (MAD)103.165
Skewness16.151821
Sum786856.62
Variance9375002.4
MonotonicityNot monotonic
2022-12-28T09:18:44.033035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.87 4
 
0.4%
18.99 4
 
0.4%
19.07 4
 
0.4%
19.02 4
 
0.4%
18.85 3
 
0.3%
18.72 3
 
0.3%
228.7 2
 
0.2%
332.53 2
 
0.2%
18.58 2
 
0.2%
25.59 2
 
0.2%
Other values (1087) 1104
97.4%
ValueCountFrequency (%)
0.42 1
0.1%
1.57 1
0.1%
1.79 1
0.1%
1.87 1
0.1%
1.99 1
0.1%
2.86 1
0.1%
3.63 1
0.1%
4.01 1
0.1%
4.23 1
0.1%
4.49 1
0.1%
ValueCountFrequency (%)
74384.64 1
0.1%
42162.1 1
0.1%
32222.54 1
0.1%
19541.36 1
0.1%
15385.88 1
0.1%
15129.31 1
0.1%
11100.11 1
0.1%
10924.17 1
0.1%
9795.49 1
0.1%
9376.99 1
0.1%

2016
Real number (ℝ)

Distinct1098
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean715.14457
Minimum0.48
Maximum77455.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-12-28T09:18:44.079824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile16.1
Q149.03
median131.425
Q3428.025
95-th percentile2795.9655
Maximum77455.23
Range77454.75
Interquartile range (IQR)378.995

Descriptive statistics

Standard deviation3175.7719
Coefficient of variation (CV)4.4407412
Kurtosis342.67585
Mean715.14457
Median Absolute Deviation (MAD)107.845
Skewness16.316584
Sum810973.94
Variance10085527
MonotonicityNot monotonic
2022-12-28T09:18:44.254397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.94 4
 
0.4%
18.99 3
 
0.3%
18.98 3
 
0.3%
18.8 3
 
0.3%
18.66 2
 
0.2%
18.79 2
 
0.2%
45.26 2
 
0.2%
17.61 2
 
0.2%
18.96 2
 
0.2%
18.53 2
 
0.2%
Other values (1088) 1109
97.8%
ValueCountFrequency (%)
0.48 1
0.1%
1.71 1
0.1%
1.85 1
0.1%
2.19 1
0.1%
2.21 1
0.1%
2.99 1
0.1%
3.22 1
0.1%
3.67 1
0.1%
4.13 1
0.1%
4.21 1
0.1%
ValueCountFrequency (%)
77455.23 1
0.1%
43816.58 1
0.1%
33638.65 1
0.1%
20436.38 1
0.1%
15758 1
0.1%
15448.68 1
0.1%
11702.09 1
0.1%
11386.25 1
0.1%
9990.27 1
0.1%
9078.64 1
0.1%

2017
Real number (ℝ)

Distinct1099
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean732.2367
Minimum0.53
Maximum79643.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-12-28T09:18:44.302323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.53
5-th percentile16.2315
Q150.905
median133.835
Q3430.6975
95-th percentile2918.407
Maximum79643.01
Range79642.48
Interquartile range (IQR)379.7925

Descriptive statistics

Standard deviation3261.2407
Coefficient of variation (CV)4.4538066
Kurtosis344.54492
Mean732.2367
Median Absolute Deviation (MAD)109.785
Skewness16.375394
Sum830356.42
Variance10635691
MonotonicityNot monotonic
2022-12-28T09:18:44.347331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.89 4
 
0.4%
19.17 3
 
0.3%
19.28 3
 
0.3%
19.23 3
 
0.3%
19.33 3
 
0.3%
18.9 3
 
0.3%
97.7 2
 
0.2%
32.66 2
 
0.2%
32.46 2
 
0.2%
15.66 2
 
0.2%
Other values (1089) 1107
97.6%
ValueCountFrequency (%)
0.53 1
0.1%
1.87 1
0.1%
2.34 1
0.1%
2.4 1
0.1%
2.66 1
0.1%
2.74 1
0.1%
3.13 1
0.1%
3.39 1
0.1%
3.97 1
0.1%
4 1
0.1%
ValueCountFrequency (%)
79643.01 1
0.1%
45081.09 1
0.1%
34561.92 1
0.1%
21108.17 1
0.1%
15942.85 1
0.1%
15718.28 1
0.1%
12088.35 1
0.1%
11891.76 1
0.1%
10247 1
0.1%
9216.41 1
0.1%

2018
Real number (ℝ)

Distinct1101
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean735.35849
Minimum0.62
Maximum80241.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-12-28T09:18:44.394788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.62
5-th percentile15.7515
Q151.3425
median136.205
Q3434.2075
95-th percentile2952.0795
Maximum80241.83
Range80241.21
Interquartile range (IQR)382.865

Descriptive statistics

Standard deviation3280.3179
Coefficient of variation (CV)4.4608418
Kurtosis346.61113
Mean735.35849
Median Absolute Deviation (MAD)112.505
Skewness16.432022
Sum833896.53
Variance10760485
MonotonicityNot monotonic
2022-12-28T09:18:44.439729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.61 3
 
0.3%
20.87 3
 
0.3%
20.45 3
 
0.3%
20.71 3
 
0.3%
20.76 3
 
0.3%
59.18 2
 
0.2%
577.63 2
 
0.2%
20.98 2
 
0.2%
20.83 2
 
0.2%
21.06 2
 
0.2%
Other values (1091) 1109
97.8%
ValueCountFrequency (%)
0.62 1
0.1%
2.19 1
0.1%
2.44 1
0.1%
2.81 1
0.1%
2.88 1
0.1%
3.09 1
0.1%
3.58 1
0.1%
3.83 1
0.1%
4.53 1
0.1%
4.61 1
0.1%
ValueCountFrequency (%)
80241.83 1
0.1%
45322.42 1
0.1%
34919.41 1
0.1%
20753.48 1
0.1%
16374.32 1
0.1%
15547.74 1
0.1%
12213.71 1
0.1%
11657.61 1
0.1%
10501.1 1
0.1%
9095.87 1
0.1%

2019
Real number (ℝ)

Distinct1097
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean753.51953
Minimum0.57
Maximum82471.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-12-28T09:18:44.487353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.57
5-th percentile16.683
Q152.4925
median137.405
Q3446.715
95-th percentile3010.527
Maximum82471.86
Range82471.29
Interquartile range (IQR)394.2225

Descriptive statistics

Standard deviation3370.6081
Coefficient of variation (CV)4.4731529
Kurtosis346.85058
Mean753.51953
Median Absolute Deviation (MAD)113.48
Skewness16.441986
Sum854491.15
Variance11360999
MonotonicityNot monotonic
2022-12-28T09:18:44.529498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.37 4
 
0.4%
17.51 4
 
0.4%
17.5 3
 
0.3%
17.84 3
 
0.3%
17.67 3
 
0.3%
17.95 3
 
0.3%
17.88 2
 
0.2%
313.81 2
 
0.2%
14.41 2
 
0.2%
35.5 2
 
0.2%
Other values (1087) 1106
97.5%
ValueCountFrequency (%)
0.57 1
0.1%
1.96 1
0.1%
2.53 1
0.1%
2.82 1
0.1%
3.19 1
0.1%
3.28 1
0.1%
3.33 1
0.1%
3.67 1
0.1%
4.15 1
0.1%
4.47 1
0.1%
ValueCountFrequency (%)
82471.86 1
0.1%
46429.24 1
0.1%
36042.62 1
0.1%
21652.46 1
0.1%
16769.36 1
0.1%
15730.23 1
0.1%
12602.42 1
0.1%
12155.76 1
0.1%
10679.05 1
0.1%
9496.69 1
0.1%

2020
Real number (ℝ)

Distinct1099
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean761.9802
Minimum0.92
Maximum83310.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-12-28T09:18:44.573443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.92
5-th percentile16.6675
Q155.1475
median148.415
Q3450.125
95-th percentile3055.541
Maximum83310.76
Range83309.84
Interquartile range (IQR)394.9775

Descriptive statistics

Standard deviation3396.5658
Coefficient of variation (CV)4.4575512
Kurtosis349.33392
Mean761.9802
Median Absolute Deviation (MAD)118.355
Skewness16.495483
Sum864085.55
Variance11536659
MonotonicityNot monotonic
2022-12-28T09:18:44.615427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.83 4
 
0.4%
12.19 3
 
0.3%
29.35 3
 
0.3%
29.37 3
 
0.3%
35.67 3
 
0.3%
29.33 3
 
0.3%
43.36 2
 
0.2%
42.23 2
 
0.2%
28.69 2
 
0.2%
29.11 2
 
0.2%
Other values (1089) 1107
97.6%
ValueCountFrequency (%)
0.92 1
0.1%
2.97 1
0.1%
2.98 1
0.1%
3.34 1
0.1%
3.35 1
0.1%
3.62 1
0.1%
4.22 1
0.1%
4.27 1
0.1%
4.45 1
0.1%
5.12 1
0.1%
ValueCountFrequency (%)
83310.76 1
0.1%
46457.17 1
0.1%
36853.59 1
0.1%
20177.76 1
0.1%
17209.26 1
0.1%
16223.16 1
0.1%
12693.54 1
0.1%
11261.43 1
0.1%
10897.25 1
0.1%
9340.41 1
0.1%

Interactions

2022-12-28T09:18:42.682564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:37.857175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.406223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.842551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.351632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.794692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.237224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.757865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.210116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.663945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.143980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.719805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:37.922751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.445546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.881043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.391324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.834272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.276598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.798081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.251966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.704339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.293279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.757836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:37.989796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.484834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.919893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.431036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.873739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.316008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.838323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.293303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.745373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.334288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.794798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.063852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.523478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.033404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.470675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.913465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.355631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.879517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.333855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.785771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.374983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.833404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.128298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.564156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.074272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.511332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.954473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.473725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.921109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.375971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.827239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.415068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.871575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.168542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.604511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.114766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.552515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.995284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.515557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.963212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.417602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.868923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.454179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.910263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.209015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.644390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.154771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.593580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.036216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.556569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.005100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.459220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.910493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.493299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.950144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.250481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.686157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.195683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.635749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.078261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.599593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.047636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.502511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.953273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.532764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.989814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.291604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.727427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.237328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.677890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.120481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.641587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.091013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.544928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.999943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.572603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:43.028843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.333200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.769109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.278596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.719987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.162661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.683349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.134016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.587811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.056514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.612800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:43.066443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.369933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:38.806088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.314826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:39.757376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.200193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:40.720562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.171863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:41.626100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.101597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T09:18:42.647580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-12-28T09:18:44.656320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-28T09:18:44.720963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-28T09:18:44.777017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-28T09:18:44.832957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-28T09:18:44.885311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-28T09:18:44.933594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-28T09:18:43.127869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-28T09:18:43.203553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MasseinheitLeistungGeschlechtAltersklasse20102011201220132014201520162017201820192020
0AbsolutwertLeistung - TotalGeschlecht - TotalAltersklasse - Total62564.9864242.6966512.4469118.0371429.2274384.6477455.2379643.0180241.8382471.8683310.76
1AbsolutwertLeistung - TotalGeschlecht - Total0-5 Jahre1058.291081.821142.631201.471233.521286.221350.121397.851420.241446.211459.25
2AbsolutwertLeistung - TotalGeschlecht - Total6-10 Jahre741.94763.72791.50833.56868.77901.73958.47979.50984.541030.281005.60
3AbsolutwertLeistung - TotalGeschlecht - Total11-15 Jahre1167.691176.461187.571230.131274.321318.081367.321306.371257.241369.171574.17
4AbsolutwertLeistung - TotalGeschlecht - Total16-20 Jahre1954.381966.642005.312032.152120.422215.102307.302400.912424.672481.642612.83
5AbsolutwertLeistung - TotalGeschlecht - Total21-25 Jahre1965.462010.732119.642194.062259.242294.932376.532405.842391.392444.172404.88
6AbsolutwertLeistung - TotalGeschlecht - Total26-30 Jahre2386.502439.342539.032621.642722.132830.682980.353097.863129.233192.413177.82
7AbsolutwertLeistung - TotalGeschlecht - Total31-35 Jahre2762.712824.602966.203086.393215.503347.343500.123668.863698.743774.033849.67
8AbsolutwertLeistung - TotalGeschlecht - Total36-40 Jahre3196.903164.662991.233066.493123.723215.413402.903637.263649.013780.843777.05
9AbsolutwertLeistung - TotalGeschlecht - Total41-45 Jahre3632.713605.433437.493500.443522.343566.463645.103770.403706.783815.933794.40
MasseinheitLeistungGeschlechtAltersklasse20102011201220132014201520162017201820192020
1124Wert pro Kopf und MonatVerwaltungFrau51-55 Jahre32.8433.2431.7730.9730.8331.0332.5432.6032.6733.5034.40
1125Wert pro Kopf und MonatVerwaltungFrau56-60 Jahre37.2537.5535.8134.3633.9334.1835.7535.8035.9436.6137.43
1126Wert pro Kopf und MonatVerwaltungFrau61-65 Jahre43.1943.6841.2639.4538.9839.0440.9840.9941.1541.7342.47
1127Wert pro Kopf und MonatVerwaltungFrau66-70 Jahre51.1552.4149.6647.5046.9047.1248.7248.5648.7749.7250.46
1128Wert pro Kopf und MonatVerwaltungFrau71-75 Jahre61.9562.6458.8556.7156.5856.9959.4559.2859.1860.4961.45
1129Wert pro Kopf und MonatVerwaltungFrau76-80 Jahre75.6976.7672.2969.6669.4769.2372.0971.8071.9572.9274.65
1130Wert pro Kopf und MonatVerwaltungFrau81-85 Jahre95.0095.8990.3885.3285.3385.5988.5788.1089.0490.4392.66
1131Wert pro Kopf und MonatVerwaltungFrau86-90 Jahre123.04125.73117.60110.56109.59108.81111.12111.39112.46114.52119.10
1132Wert pro Kopf und MonatVerwaltungFrau91-95 Jahre164.99167.50154.12147.86143.82144.08143.55142.55143.94146.25157.40
1133Wert pro Kopf und MonatVerwaltungFrau96 und mehr Jahre216.70232.14214.66198.60199.22195.54193.25192.36190.94193.70207.38